71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import os
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
|
|
|
from mynet import onehot
|
|
|
|
|
|
HERE = os.path.abspath(os.path.dirname(__file__))
|
|
DATA = os.path.join(HERE, 'data')
|
|
CORPUS = os.path.join(DATA, 'corpus.txt')
|
|
VOCAB = os.path.join(DATA, 'vocab.txt')
|
|
TEST = os.path.join(DATA, 'test.txt')
|
|
|
|
vocab = {
|
|
w: i for i, w in enumerate(open(VOCAB).read().splitlines(keepends=False))
|
|
}
|
|
inv_vocab = sorted(vocab, key=vocab.get)
|
|
|
|
|
|
def word_tokenize(s: str):
|
|
l = ''.join(c.lower() if c.isalpha() else ' ' for c in s)
|
|
return l.split()
|
|
|
|
|
|
def create_test_dataset(win):
|
|
import numpy as np
|
|
test_dataset = np.vectorize(vocab.get)(np.genfromtxt(TEST, dtype=str))
|
|
assert test_dataset.shape[1] == 2*win + 1
|
|
X_test = test_dataset[:, [*range(0, win), *range(win+1, win+win+1)]]
|
|
y_test = onehot(test_dataset[:, win], nc=len(vocab))
|
|
return X_test, y_test
|
|
|
|
|
|
def create_mnist_network():
|
|
import tensorflow as tf
|
|
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # STFU!
|
|
tf.random.set_random_seed(42)
|
|
|
|
model = tf.keras.models.Sequential([
|
|
tf.keras.layers.Dense(30, input_shape=(784,), activation='relu'),
|
|
tf.keras.layers.Dense(10, activation='softmax')
|
|
])
|
|
model.compile(loss='categorical_crossentropy', optimizer='adam',
|
|
metrics=['accuracy'])
|
|
return model
|
|
|
|
|
|
def create_cbow_network(win, embed):
|
|
import tensorflow as tf
|
|
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # STFU!
|
|
tf.random.set_random_seed(42)
|
|
|
|
ctxt = tf.keras.layers.Input(shape=[2*win])
|
|
ed = tf.keras.layers.Embedding(len(vocab), embed, input_length=2*win)(ctxt)
|
|
cbow = tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=1))(ed)
|
|
blowup = tf.keras.layers.Dense(len(vocab), activation='softmax')(cbow)
|
|
mod = tf.keras.Model(inputs=ctxt, outputs=blowup)
|
|
mod.compile(
|
|
optimizer='adam',
|
|
loss='categorical_crossentropy',
|
|
)
|
|
return mod
|
|
|
|
|
|
def token_generator(filename):
|
|
with open(filename) as f:
|
|
for i, l in enumerate(f.readlines()):
|
|
if not l.isspace():
|
|
tok = word_tokenize(l)
|
|
if tok:
|
|
yield tok
|